Machine Learning

Unlock the power of data and artificial intelligence with this comprehensive Machine Learning course from Ucanly. Designed for those with a foundational knowledge of programming, this intermediate course will guide you through the essential theories and practical applications of machine learning. You will learn to build, train, and deploy predictive models using Python and its industry-standard libraries, including scikit-learn, Pandas, and TensorFlow.

With the expert guidance of Ucanly, you will explore the full spectrum of machine learning, from data preprocessing and feature engineering to mastering supervised and unsupervised learning algorithms. This Ucanly course emphasizes a hands-on approach, ensuring you understand not just the ‘how’ but also the ‘why’ behind concepts like regression, classification, clustering, and model evaluation. We will then bridge the gap to deep learning, giving you a solid introduction to building neural networks with TensorFlow.

By the end of this project-driven course, you will have completed a capstone project that solves a real-world problem, solidifying your skills and providing a valuable addition to your data science portfolio. This course is your definitive next step to becoming a proficient Machine Learning practitioner.

Preprocess and clean data for machine learning using Pandas and NumPy.
Build and train supervised learning models for regression and classification using Scikit-learn.
Implement unsupervised learning techniques like K-Means clustering and PCA.
Evaluate your models using a variety of performance metrics to understand their strengths and weaknesses.
Tune model hyperparameters to significantly improve performance.
Understand the core concepts of neural networks and build basic models using TensorFlow.
Complete an end-to-end machine learning project suitable for a professional portfolio.

Live Q&A and Code-Along Sessions with Instructors
On-demand video lectures
Downloadable Python notebooks and datasets for all projects
Cheatsheets for Pandas, Scikit-learn, and TensorFlow
Quizzes and practical exercises to reinforce concepts
Lifetime access to course materials and future updates
Certificate of Completion from Ucanly

Course Content

Module 1: The Python Data Science Stack (The Ucanly Foundation)
This module, curated by Ucanly, ensures you have mastered the essential tools for any data science project in Python.

Lesson 1.1: Course Introduction & The Machine Learning Landscape
Lesson 1.2: Advanced NumPy for Numerical Computation
Lesson 1.3: Data Manipulation and Analysis with Pandas
Lesson 1.4: Data Visualization with Matplotlib and Seaborn
Lesson 1.5: Setting Up Your ML Environment (Jupyter, Colab)

Module 2: Core Concepts of Machine Learning
Build a solid theoretical foundation before diving into algorithms. Understand the principles that govern model performance.

Module 3: Supervised Learning – Regression
Learn to predict continuous values and model relationships in your data.

Module 4: Supervised Learning – Classification
Master the art of building models that classify data into distinct categories.

Module 5: Unsupervised Learning (A Ucanly Deep Dive)
Discover hidden patterns and structures in unlabeled data with techniques taught by Ucanly.

Module 6: Preprocessing, Tuning, and Pipelines
Learn the professional techniques for cleaning data and optimizing model performance.

Module 7: Introduction to Deep Learning with TensorFlow
Step into the world of neural networks with Google's powerful deep learning framework.

Module 8: Capstone Project (The Ucanly Challenge)
Apply all your acquired knowledge to a comprehensive, real-world data science problem from start to finish.

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Price:
Original price was: ₹25,000.00.Current price is: ₹14,500.00.
Instructor:Ucanly
Duration: 60 hours
Lessons:38
Students:0
Level:Beginner

Machine Learning

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Course Content

Module 1: The Python Data Science Stack (The Ucanly Foundation) This module, curated by Ucanly, ensures you have mastered the essential tools for any data science project in Python.

Lesson 1.1: Course Introduction & The Machine Learning Landscape

Lesson 1.2: Advanced NumPy for Numerical Computation

Lesson 1.3: Data Manipulation and Analysis with Pandas

Lesson 1.4: Data Visualization with Matplotlib and Seaborn

Lesson 1.5: Setting Up Your ML Environment (Jupyter, Colab)

Module 2: Core Concepts of Machine Learning Build a solid theoretical foundation before diving into algorithms. Understand the principles that govern model performance.

Lesson 2.1: The Machine Learning Workflow: A High-Level Overview

Lesson 2.2: Supervised vs. Unsupervised vs. Reinforcement Learning

Lesson 2.3: The Bias-Variance Tradeoff: Understanding Overfitting & Underfitting

Lesson 2.4: Splitting Data: Training, Validation, and Test Sets

Lesson 2.5: Model Evaluation Metrics: Accuracy, Precision, Recall, F1-Score, RMSE

Module 3: Supervised Learning – Regression Learn to predict continuous values and model relationships in your data.

Lesson 3.1: Simple and Multiple Linear Regression

Lesson 3.2: Polynomial Regression for Non-linear Data

Lesson 3.3: Regularization: Ridge (L2) and Lasso (L1) Regression

Lesson 3.4: Project: Predicting House Prices with Scikit-learn

Module 4: Supervised Learning – Classification Master the art of building models that classify data into distinct categories.

Lesson 4.1: Logistic Regression for Binary Classification

Lesson 4.2: K-Nearest Neighbors (KNN)

Lesson 4.3: Support Vector Machines (SVMs)

Lesson 4.4: Decision Trees and Random Forests

Lesson 4.5: Project: Classifying Customer Churn

Module 5: Unsupervised Learning (A Ucanly Deep Dive) Discover hidden patterns and structures in unlabeled data with techniques taught by Ucanly.

Lesson 5.1: The Concept of Clustering

Lesson 5.2: K-Means Clustering Algorithm

Lesson 5.3: Introduction to Dimensionality Reduction

Lesson 5.4: Principal Component Analysis (PCA)

Lesson 5.5: Project: Customer Segmentation for a Retail Business

Module 6: Preprocessing, Tuning, and Pipelines Learn the professional techniques for cleaning data and optimizing model performance.

Lesson 6.1: Handling Missing Data

Lesson 6.2: Feature Scaling (Standardization & Normalization)

Lesson 6.3: Encoding Categorical Data

Lesson 6.4: Hyperparameter Tuning with GridSearchCV

Lesson 6.5: Building Efficient Workflows with Scikit-learn Pipelines

Module 7: Introduction to Deep Learning with TensorFlow Step into the world of neural networks with Google's powerful deep learning framework.

Lesson 7.1: From Machine Learning to Deep Learning

Lesson 7.2: Introduction to Artificial Neural Networks (ANNs)

Lesson 7.3: Building Sequential Models with the Keras API in TensorFlow

Lesson 7.4: Compiling and Training Your First Neural Network

Lesson 7.5: Project: Image Classification on a Simple Dataset

Module 8: Capstone Project (The Ucanly Challenge) Apply all your acquired knowledge to a comprehensive, real-world data science problem from start to finish.

Lesson 8.1: Problem Definition and Data Collection

Lesson 8.2: Exploratory Data Analysis (EDA)

Lesson 8.3: Model Selection, Training, and Evaluation

Lesson 8.4: Final Analysis and Presentation of Results

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